Atrioventricular Coupling (AV-Coupling) refers to the functional coordination between atrial and ventricular systole and diastole in the heart. Currently, the primary method for evaluating AV-Coupling is through the left atrioventricular coupling index (LACI), measured using imaging techniques. A higher LACI indicates a greater mismatch between the volumes of left atrium and left ventricle at the end of ventricular diastole, reflecting a more significant impairment of left AV-Coupling. AV-Coupling plays a vital role in the pathophysiology and progression of cardiovascular diseases. Therefore, early and accurate assessment of AV-Coupling is essential for evaluating a patient’s condition, guiding clinical decisions, stratifying risk, and determining prognosis. This review aims to summarize the physiological mechanisms and evaluation methods of AV-Coupling, as well as its clinical significance in various cardiovascular diseases.

Ultrasound radiogenomics, an emerging field at the intersection of radiology and genomics, employs high-throughput methods to convert radiological images into high-dimensional data, which are then processed to extract and analyze radiomic features. These features, including shape, texture, and intensity variations, are correlated with specific genetic mutations such as TP53 and PIK3CA, critical for cancer progression and treatment response. By integrating clinical data with ultrasonic features, predictive models are developed using machine learning techniques, aiming to refine the capability to diagnose and personalize treatment plans for breast cancer patients. This approach reduces the need for invasive biopsies and medical costs for patients through a better understanding of the tumor’s biological behavior using ultrasound images. This review focuses on the application of ultrasound radiogenomics for predicting gene mutations in breast cancer, highlighting its transformative potential in clinical practice and discussing ongoing challenges and future directions in this field.

Since 2020, breast cancer has held the highest incidence rate among cancers worldwide. Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. This review focuses on the application of convolutional neural networks (CNNs) within DL technology in the field of breast US. It summarizes the use of DL models in breast cancer screening and in preoperative prediction of molecular subtypes, response to neoadjuvant chemotherapy (NAC), and axillary lymph node (ALN) metastasis status. The review also identifies the data limitations of using CNN models in breast US and describes the development history and current applications of DL in breast cancer screening, diagnostic guidance, and prognostic prediction. Furthermore, it discusses the future research directions and potential challenges. Advancing the development of CNN technology in breast US, and improving the generalizability and reproducibility of these models, will significantly promote their translational application in clinical settings.

Lymphoma is a common hematological malignancy with markedly increasing incidence. Its pathological types are complex and heterogeneous, and there are significant differences in treatment options and efficacy. Therefore, early and precise diagnosis, assessment of efficacy, and judgment of prognosis are key clinical problems. Ultrasound (US) has important clinical value in the diagnosis and treatment of lymphoma. This article reviews the progress made with new US technologies in improving the accuracy of diagnosis and staging of lymphoma, predicting the course of lymphoma, monitoring the progression of lesions during treatment, and assisting clinics in formulating accurate and effective treatment plans. In addition, we review the biological basis of US prediction of lymphoma and provide an outlook for future research directions.

Papillary thyroid microcarcinoma (PTMC) is a subtype of papillary thyroid carcinoma (PTC) characterized by a diameter of less than 10 mm. While its incidence is on the rise, PTMC generally carries a favorable prognosis. Traditional surgical intervention remains the primary treatment method, widely recognized for its effectiveness. However, surgical procedures can lead to postoperative scarring and complications, posing challenges for patients. For some low-risk PTMC cases that exhibit long periods of non-progression, active surveillance has emerged as a viable treatment option. Thermal ablation technology, guided by ultrasound, has demonstrated comparable short-term efficacy to surgery but with smaller incisions and reduced costs, offering a new alternative for PTMC patients. Currently, the management strategies for PTMC exhibit considerable diversity, contributing to ongoing debates in treatment approaches. This paper provides a comprehensive summary and review of the primary therapies available today.

Preoperative imaging is crucial for patients diagnosed with renal cell carcinoma presenting with thrombus. These individuals frequently exhibit a hypercoagulable state, raising the risk of thrombus progression or the formation of a new bland thrombus post-imaging and pre-surgery. Intraoperative ultrasound, employed under direct visualization, offers real-time, dynamic detection of thrombi, potentially influencing surgical decisions. This short review explores the utility of intraoperative ultrasound in robot-assisted thrombectomy for renal cell carcinoma, detailing its primary applications and added value in mitigating surgical risks for urologists.

Alzheimer’s disease (AD) is a common neurodegenerative disease in clinical practice. The pathogenesis is still unclear, and there is no specific method. According to the current known pathological studies, AD biomarker TAU protein, phosphorylated tau and amyloid-β (Aβ) play an important role in the pathophysiological changes of AD. For pathological research, the development of low-intensity ultrasound (LIUS) provides another idea for the mechanism of AD treatment, which can better treat AD, regulate various factors specifically, and effectively treat AD by stimulating synapses and improving neurons. Based on this research background, this paper summarizes the role of AD biomarkers TAU protein, phosphorylated tau and amyloid protein in the occurrence and development of AD and the mechanism of pathological changes in the treatment of AD by low-intensity ultrasound, aiming to provide new insights into clarifying the pathological changes of AD biomarkers and the mechanism of LIUS in the treatment of AD. Given that the treatment for AD based on LIUS is still far from a complete cure, we will discuss the prospects for future development of LIUS to guide the treatment of AD.

Barium titanate (BaTiO₃), as an emerging inorganic piezoelectric material with excellent piezoelectric catalytic effects, has showing advantages in tumor therapy. To achieve ultrasound-regulated tumor treatment using BaTiO₃, researchers have developed strategies including utilizing BaTiO₃ combined with ultrasound for tumor therapy, enhancing reactive oxygen species (ROS) generation through chemical modification of BaTiO₃, and employing combined therapy with other treatment methods. These strategies provide new insights and approaches for non-invasive and precision treatment of tumors. In this review, we first explain the principle of piezoelectric effect based on BaTiO₃. Subsequently, we introduce the application of BaTiO₃ as a piezoelectric material in tumor therapy and its combined therapy with other treatment modalities in tumor treatment. Finally, we summarize the current status and limitations of BaTiO₃ in ultrasound‐triggered piezoelectric therapy for tumors and propose future prospects.

Multiparametric MRI (mpMRI) is currently the standard imaging modality for the diagnosis of prostate cancer; however, studies have reported that targeted biopsy based on mpMRI may miss approximately 30% of clinically significant cases. Recent advances in ultrasound imaging have improved its accuracy for detection of prostate cancer. Newer techniques such as MicroUS, elastography, contrast-enhanced ultrasound (CEUS), and contrast ultrasound dispersion imaging (CUDI) have enabled a comprehensive, real-time, and relatively inexpensive approach to evaluate the prostate gland. Multiparametric ultrasound (mpUS) integrates multiple parameters from these techniques to generate multiparametric maps akin to those produced by mpMRI, to localize prostate cancer. This review aims to explore the performance of modern ultrasound techniques and mpUS for diagnosis of prostate cancer, comparing them with mpMRI.

Right ventricular-pulmonary artery coupling (RV-PAC) serves as an indicator of the efficiency of energy transfer from the right ventricle to the pulmonary circulation. It plays a critical role in the diagnosis, clinical treatment, and prognosis of conditions such as pulmonary hypertension, heart valve disease, and heart failure. Various non-invasive evaluation methods have recently been proposed for assessing RV contractility and arterial afterload, based on the end-systolic elastance to arterial elastance ratio (Ees/Ea), which is derived from invasive pressure-volume loops. In this review, we summarize the fundamental concepts, physiological mechanisms, examination methods, influencing factors, and clinical significance of RV-PAC to provide a valuable reference for clinical practice.

The left ventricle (LV) and right ventricle (RV) are interdependent, as both structures are nestled within the pericardium, share a common septum, and are encircled by interconnected myocardial fibers. Interventricular interaction refers to the dynamic relationship between LV and RV, particularly how changes in one ventricle influence the geometry and function of the other. Imaging, particularly echocardiography, is vital for characterizing interventricular interactions by assessing geometric indices, septal motion, Doppler flow patterns, and changes in strain, remodeling, and diastolic filling associated with the loading conditions of the contralateral ventricle. In this review, we summarized the physiological and anatomical basis of ventricular interaction, echocardiographic imaging indices, and their clinical utilities and limitations. The goal is to systematically review the research advancements in echocardiographic assessment of LV-RV coupling and to provide guidance for clinical practice.

Right ventricular-pulmonary arterial coupling refers to the interaction and functional matching between the right ventricle and the pulmonary artery. When the coupling is disrupted, it can lead to a series of cardiovascular diseases, such as pulmonary hypertension, congenital heart disease, heart failure and so on. Therefore, it is important to evaluate cardiovascular structure and function. Cardiac magnetic resonance has the advantage of multi-parameter, multi-sequence, and high-resolution imaging, which can be used to comprehensively evaluate the cardiovascular system through cardiac magnetic resonance feature tracking technology, cardiac magnetic resonance cine imaging technology, T1 mapping, and T2 mapping imaging, and so on. This review summarizes the application and research progress of cardiac magnetic resonance technology in the assessment of the right ventricle and the pulmonary artery (RV-PA) coupling.

Arterial stiffness (AS) represents a pathological process characterized by reduced arterial elasticity and compliance, closely linked to aging and cardiovascular diseases, including hypertension, atherosclerosis, diabetes, and chronic kidney disease. As an important predictor of cardiovascular risk, AS evaluation plays a crucial role in early detection, disease monitoring, and therapeutic guidance. This review aims to systematically summarize current advancements in AS evaluation, focusing on non-invasive techniques such as pulse wave velocity, ultrasound-based methods, and arterial pressure waveform analysis. We discuss the advantages, limitations, and clinical applications of these methods, highlighting the recent integration of artificial intelligence and machine learning to enhance diagnostic accuracy and automation. The review also explores emerging biomarkers and novel imaging techniques, such as shear wave elastography and ultrafast ultrasound imaging, which offer promising insights for early AS detection and risk stratification. Despite significant progress, challenges remain in standardizing measurement protocols and improving sensitivity across various populations. Future research directions emphasize the development of wearable technologies, artificial intelligence-based diagnostic tools, and standardized methodologies to advance AS evaluation and improve cardiovascular outcomes.

Left ventricular-arterial coupling (LVAC) represents a critical physiological mechanism that characterizes the interaction between left ventricular (LV) contractility and the arterial system's resistance and elasticity. The balance within LVAC is essential for efficient energy transfer from the heart, which underpins optimal cardiovascular function. In a healthy state, the balance between LV contractility and arterial elasticity and resistance allows the heart to maintain normal circulation with minimal energy expenditure. However, with the progression of age and diseases such as atherosclerosis and hypertension, arterial stiffness increases, LV function decreases, and the LVAC balance is disrupted, leading to a significantly increased risk of cardiovascular events. This imbalance is particularly significant in patients with heart failure (HF) and coronary artery disease (CAD), where LVAC imbalance is strongly associated with increased cardiac load and decreased energy efficiency. Thus, understanding and evaluating LVAC are crucial for elucidating cardiovascular physiology and guiding therapeutic strategies for diseases such as HF, hypertension, and CAD. Methods for assessing LVAC include invasive pressure-volume loops and cardiac catheterization, as well as non-invasive techniques such as echocardiography and arterial pulse wave analysis (PWA). Despite the higher accuracy of invasive methods, non-invasive methods are commonly used in clinical practice to assess LVAC because of their lower risk. With cardiac magnetic resonance imaging (CMR) and 3D/4D imaging techniques advancing, more precise structural and functional analysis of the heart and arterial system will be possible in the future. In this review, we describe the physiological mechanisms, assessment methods, influencing factors, and clinical significance of LVAC, as well as interdisciplinary studies with biomechanics and metabolism, which provide new ideas for personalized treatment of LVAC.

Left ventricular-arterial coupling (VAC) is essential for understanding both cardiovascular physiology and pathophysiology. Traditionally assessed through invasive techniques, recent advancements have introduced noninvasive methods that employ imaging modalities and physiological parameters to evaluate ventricular pressure, volume, and arterial load characteristics. This review examines commonly used noninvasive VAC assessment methods, including echocardiographic single-beat method, myocardial work, wave intensity, the ratio of pulse wave velocity to global longitudinal strain, and imaging-based pressure-volume loops. These methodologies have demonstrated potential in clinical applications, such as evaluating cardiac function, personalizing treatment plans, monitoring therapeutic effects, and assessing prognosis. The incorporation of advanced imaging and computational techniques is anticipated to further enhance the accuracy and clinical relevance of VAC assessment in the management of cardiovascular diseases.

A heterozygous microdeletion of chromosome 7q11.23 causes the rare neuropsychiatric developmental disorder Williams-Beuren Syndrome. The syndrome is more difficult to diagnose before birth than after, even though the syndrome often manifests prenatally as intrauterine growth restriction and cardiovascular defects on prenatal ultrasonography. The potentially poor prognosis of affected individuals highlights the need to improve prenatal diagnosis of the syndrome. This review summarizes recent advances in our understanding of the genetics of Williams-Beuren Syndrome and its manifestations on prenatal ultrasonography, which may facilitate its early detection and inform prenatal genetic counseling.

Ultrasound imaging holds a significant position in medical diagnostics due to its non-invasive and real-time capabilities. However, traditional ultrasound is constrained by the diffraction limit, making it challenging to capture fine blood vessels. Ultrasound localization microscopy (ULM) overcomes this limitation by achieving super-resolution imaging through tracking the trajectories of microbubbles (MBs) within microvasculature. This review summarizes the applications of deep learning (DL) techniques in ULM post-processing algorithms, including key steps such as beamforming, clutter filtering and denoising, localization, and tracking. Although DL shows great potential in improving ULM imaging quality and efficiency, current research mainly focuses on imaging algorithmic improvements rather than in-depth image analysis. In the future, with the accumulation of ULM image data, the powerful feature extraction capability of DL is expected to further advance ULM applications in disease prediction and diagnosis.

Integrating machine learning into medical diagnostics has revolutionized the field, particularly enhancing Computer-aided Diagnosis (CAD) systems. These systems assist healthcare professionals by leveraging medical data and machine learning algorithms for more accurate diagnosis and treatment plans. Mammography, an X-ray-based imaging technique, is pivotal in early breast cancer detection, enabling the differentiation between benign and malignant lesions. Recent studies have focused on developing deep learning-enabled mammography CAD systems, which have shown promising results in detecting, segmenting, and classifying anomalies in mammogram images. This comprehensive review presents an innovative system architecture for breast cancer detection, segmentation, and classification using deep learning within mammography CAD systems. It also explores publicly available mammogram datasets and the critical parameters for assessing deep learning system performance. The literature review is meticulously conducted using the PRISMA methodology to evaluate and synthesise novel research findings in this domain. This survey highlights the technological advancements and underlines the potential of deep learning in transforming mammographic analysis for breast cancer detection.

Throughout pregnancy, maternal thyroid-related hormones are transported to the fetus via the placenta to allow normal fetal growth and development and are particularly important in the first and second trimesters of pregnancy. During maternal-fetal transport, in addition to thyroid-related hormones, thyroid-stimulating hormone receptor antibodies and antithyroid drugs can enter the fetus and interfere with development of the fetal thyroid gland and endocrine function, potentially leading to hyperthyroidism or hypothyroidism in the fetus or newborn. Several basic studies have been performed to demonstrate the important role of thyroid-related hormones in fetal and neonatal bone development. Ultrasound can assess neonatal skeletal maturity and bone development safely, rapidly, and effectively. This review aims to communicate the latest knowledge about maternal and fetal thyroid function in both normal and pathological pregnancies and summarize the latest advances in the potential effects of abnormal maternal thyroid function on bone development in the fetus and neonate. Finally, it discusses recent advances in research on ultrasound in the assessment of fetal and neonatal bone development.